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Record Nr. |
UNINA9910484553803321 |
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Titolo |
Numerical Nonsmooth Optimization : State of the Art Algorithms / / edited by Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XVIII, 700 p. 41 illus., 26 illus. in color.) |
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Disciplina |
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Soggetti |
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Operations research |
Management science |
Decision making |
Numerical analysis |
Data mining |
Economics |
Operations Research, Management Science |
Operations Research/Decision Theory |
Numeric Computing |
Data Mining and Knowledge Discovery |
Economic Theory/Quantitative Economics/Mathematical Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods. |
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Sommario/riassunto |
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Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and |
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gradient sampling methods. In turn, the second focuses on methods that exploit the problem’s special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization. |
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